********************************************************************************
* Inflation Expectations Comparison
********************************************************************************
clear
clear matrix
set more off
set scheme s1color
estimates clear
graph drop _all
set matsize 2500
log close _all
graph set window fontface "Times New Roman"

** Set Directory
cd "../Do"
** Set Haver Directory
// set haverdir "Haver", perm

********************************************************************************
** HAVER PULL DATA
********************************************************************************
** Import Data
*MB importing data now through October 2013 (instead of only through October 2012)
*note that inflation values are monthly--could try changing tvar to yearq and do 4-quarter inflation instead of 12-month inflation
import haver (pcuslfe pztexp jcxfebm pzrgus cinf1)@usecon , tvar(yearmo) clear fin(2008m9, 2013m10)
ren pcuslfe_usecon cpiu
ren pztexp_usecon pztexp // deflate by pce 
ren jcxfebm_usecon pce
ren pzrgus_usecon reggas
ren cinf1_usecon mich
*MB changing denominator here to L12.cpiu instead of cpiu
tsset yearmo
*gen inflation_yearly = ((cpiu - L12.cpiu) / cpiu)*100
gen inflation_yearly = ((cpiu - L12.cpiu) / L12.cpiu)*100
la var inflation_yearly "Actual Inflation"
gen inflation_yoy_forward = F12.inflation_yearly
gen inflation_yoy_forward2 = ((F12.cpiu-cpiu)/cpiu)*100

gen wti = pztexp/pce
*WTI stands for West Texas Intermediate Crude Oil Price
la var wti "WTI, Deflated by PCE"
*reggas= "regular gas"?
gen reggas2 = reggas/100
*gas price year-over-year percent change
gen gas_infl=((reggas-L12.reggas)/L12.reggas)*100
la var gas_infl "Gas Price Percent Change"

*extrdate year year = yearmo
*extrdate quarter quarter = yearmo
gen year=yofd(dofm(yearmo))
gen yearq=qofd(dofm(yearmo))
format yearq %tq

keep if yearmo>=tm(2008m10)
keep if yearmo<=tm(2013m11)
tempfile haverd
save `haverd'

********************************************************************************
** LOAD AND CLEAN DURABLES DATA
********************************************************************************

use ../Data/matched_data_durables_jun2018_baseline.dta, clear

*labelling the 3 different durables spending variables; all are composites; one is nominal; another is "real" deflated using a single (appliances) cpi; another is "real" deflated on a good-by-good basis (in previous merge file)
la var durables "nominal durable goods spending"
la var durables_real1 "real durable goods spending, single deflator"
la var durables_real2 "real durable goods spending, separate deflators"

drop ethnicity

recode mort (5=0)
recode stocks (5=0)
recode retacct (5=0)
recode howner (5=0)

* Recode some expectations variables from the Inflation surveys. 

* unemployment dummies
recode q6 (1=1) (2 3 = 0), gen(unemp_increase)
recode q6 (3=1) (1 2 = 0), gen(unemp_decrease)

gen conditions_12m = q2a
* note everybody who says "other" is in separate category
recode q2a (1=1) (2 3 = 0), gen(conditions_12m_better)
recode q2a (2=1) (1 3 = 0), gen(conditions_12m_worse)

gen interestrate_12m = q7
recode interestrate_12m (1=1) (2 3 = 0), gen(intrate_12m_up)
recode interestrate_12m (3=1) (1 2 = 0), gen(intrate_12m_down)

gen bconditions_12m = q4
* note everybody who says "other" is in separate category
recode q4 (1=1) (2 3 = 0), gen(bconditions_12m_better)
recode q4 (2=1) (1 3 = 0), gen(bconditions_12m_worse)

* house price forecasts

gen hppoint = .
replace hppoint = 0 if q41==3
replace hppoint = q42 if q41==1
replace hppoint = -q42 if q41==2
* there are some extreme outliers (in the tens of thousands)
replace hppoint = . if abs(hppoint)>=200

la var hppoint "House price expectation"

*** Prepare some additional descriptives
* Race isn't reported in all periods. 
preserve
collapse (mean) race , by(prim_key)
sort prim_key
tempfile race
save `race'
restore

drop race
sort prim_key
merge m:1 prim_key using `race'
tab _merge
drop _merge
drop if prim_key==""

recode race (1=1) (nonmissing = 0), gen(white)
gen nonwhite = 1-white
recode gender (2=1) (1=0), gen(female)
recode highesteducation (4 9 = 0) (10/16 = 1), gen(coll)

* q31s* gives codes. Kind of odd that they've created separate variables. 
*MB: odd to generate employed variable, because everyone in sample is employed--however some also say they are retired (?) 
gen employed = . 
replace employed=1 if q31s1==1
replace employed = 0 if (q31s2==2 | q31s3==3 | q31s4==4 | q31s5==5 | q31s6==6 | q31s7==7) & q31s1!=1

* generate currently retired variable.
*odd that some are retired even though all are employed; how do retired people have wage growth expectations? they may be working a small job anyway
drop retired 
gen retired = . 
replace retired = 1 if q31s5==5
replace retired = 0 if (q31s1==1 | q31s2==2 | q31s3==3 | q31s4==4 | q31s6==6 | q31s7==7) & q31s5!=5
tab retired

*below also added December 2019 from Arman's old code
gen gas_expect = . 
replace gas_expect = 0 if q47a==3
replace gas_expect = q47a_higher if q47a==1
replace gas_expect = -q47a_lower if q47a==2

*MB resume: add "howner_fix" code ?

*replace howner=0 if howner==.
*adds ten people to homeowner status; there are still some with howner_fix==0 who have positive mortgage payment, but missing data for has mortgage and/or mortgage amount
*two observations have howner==0 and mort==1 (say they're not a homeowner but they say they have a mortgage); not important so not recoding them for now

drop if mort==0 & amtmort!=. & amtmort>100
*fixing the mort indicator to impute plausible values for people based on other information
replace mort = 0 if (howner!=1 | mortgage==0) & mort==.
replace mort = 1 if (howner==1 | (mortgage>0 & mortgage!=.)) & mort==.

la var mort "Has Mortgage"

*MB: generating quasi-continuous income variable based on midpoint of ranges of annual household income variables (familyincome and familyincome_part2)
rename familyincome inc2
rename familyincome_part2 inc2_2
drop if inc2==.
*here is the income variable: "new_faminc"
gen new_faminc=.
replace new_faminc=2500 if inc2==1
replace new_faminc=6250 if inc2==2
replace new_faminc=8750 if inc2==3
replace new_faminc=11250 if inc2==4
replace new_faminc=13750 if inc2==5
replace new_faminc=17500 if inc2==6
replace new_faminc=22500 if inc2==7
replace new_faminc=27500 if inc2==8
replace new_faminc=32500 if inc2==9
replace new_faminc=37500 if inc2==10
replace new_faminc=45000 if inc2==11
replace new_faminc=55000 if inc2==12
replace new_faminc=67500 if inc2==13
replace new_faminc=87500 if inc2==14 & (inc2_2==1 | inc2_2==.)
*above accounts for one person with inc2==14 and inc2_2 missing; not sure why that's the case but I asigned them the lowest category of income over $75000
replace new_faminc=112500 if inc2==14 & inc2_2==2
replace new_faminc=162500 if inc2==14 & inc2_2==3
replace new_faminc=237500 if inc2==14 & inc2_2==4

gen log_new_faminc=log(new_faminc)

sort prim_key quarter
*bringing in SAMPLE WEIGHTS, to quarterly data: warning, we might lose observations if the set requiring weights has changed ; this will affect our ability to run regs using non-employed types (can only do unweighted) 
merge m:1 prim_key using ../Data/qweights_pooled.dta
*these are the reg weights, the full sample weights will still be called weight_full
*weight variable is called "weight_samp" to indicate the weights were designed for the regression sample
*134 observations dropped that didn't merge with a weight_samp
drop if _merge!=3
drop _merge

*MB December 2019: key juncture: impose different sample restrictions and use different real/nominal durables
*********************
*define regression sample before recentering any variables: dropping extreme values
*below should drop top 2 highest values of durables spending; I doubt it makes any difference 
*JIMIN: try turning on and off the following restrictions
drop if durables>20000  | durables_real1>20000 | durables_real2>20000
drop if prim_key=="5041140:1"
drop if mortgage>200000 & mortgage!=.
drop if d_inflmedian>35
drop if d_longinflmedian>35
drop if hppoint<-50
**end of optional restrictions 

*assigning locals for weights (can turn on or off in regression)
local weights "[pweight=weight_samp]"
local weights_full "[pweight=weight_full]"
local pwfile "_pw"
*drop those with missing values for regressors
la var d_inflmedian "Inflation Expectation (SR)"
la var d_infliqr "Inflation Uncertainty (SR)"
la var d_longinflmedian "Inflation Expectation (MR)"
la var d_longinfliqr "Inflation Uncertainty (MR)"
la var lag_IE "Lagged Inflation Expectation (SR)"
la var lag_infl_iqr "Lagged Infl. Uncertainty (SR)"
la var hppoint "House price expectation"
la var gas_expect "Gas price expectation"

*variable for sum of monthly payments--interactions between this variable and IE may be included in some models
gen payments=mortgage+car if howner==1
replace payments=rent+car if howner!=1
gen log_payments=.
replace log_payments=log(payments) if payments>0
replace log_payments=0 if payments==0
la var log_payments "Fixed Mnthly Paymnts (Log)"
*drop any observations with extreme value for payments (110,000): only if running a regression interacting with payments: actually none dropped here (unlike nondurables)
drop if payments>100000

*defining sample; removing those with missing values
drop if weight_samp==.
drop if d_inflmedian==.
drop if d_infliqr==.
drop if durables==.
*dropping lagged IE: only need if we include lag IE in regression 
drop if lag_IE==.
drop if lag_infl_iqr==.
*new income variable: new_faminc is recode of categorical variables familyincome and familyincome_part2; former variable was earnings last month and highly unreliable
drop if new_faminc==.
drop if intrate_12m_up==.
drop if intrate_12m_down==.
drop if unemp_increase==.
drop if unemp_decrease==.
drop if rw_expect==.
drop if d_wageiqr==.
drop if rage==.
drop if nonwhite==.
drop if female==.
drop if coll==.
drop if retired==.
drop if mort==.
*below results in loss of 300+ observations--results are robust not imposing this restriction and omitting hppoint from regressions
drop if hppoint==.
*below drops 111 observations--robustness applies again 
drop if howner==.

destring prim_key, generate(id_new) ignore(":")

*generate total durables spending within household
*add to below: use durables_real1 and durables_real2 instead--number of observations of "durables" per household will be identical to that for either "durables_real1" or "durables_real2", so no need to repeat below for alternate versions
egen tot_durables=total(durables), by(id_new)
sum tot_durables, d
*below drops 142 observations associated with households who never purchased durables under period of observation (reported zero spending on durables) 
drop if tot_durables==0
*generate variable that equals 1 in all cases, to sum to determine observations per person
gen pre_obs=1
*generate sum of observations per person
egen obs2=total(pre_obs), by(id_new)
sum obs2, d

*restrict on having nonzero durables spending in at least one period (based on total durables spending within household) 
*define sample  based on sufficient observation
gen durables_sample_hp=(obs2>=3)

*Using the above sample, we now generate the median inflation expectation by quarter (based on the quarter of the month in which the expectation was formed) 
 *time series variable is a calendar quarter

gen quarter2 = qofd(expectations_date)
ren quarter2 yearq
format yearq %tq

*this way collapses by expectations quarter ("yearq"); can use only one version
collapse (p50) d_inflmedian (mean) d_infmean = d_inflmedian  if durables_sample_hp==1 [pweight=weight_samp], by(yearq)
tsset yearq
*MB changes 8/12/2019--start changes here (look for "end changes here" below)
*gen L_lag_IE = l4.d_inflmedian //Lagged Med
ren d_inflmedian d_inflmedian_dur
ren d_infmean d_infmean_dur
*ren L_lag_IE L_lag_IE_dur
drop if yearq<tq(2009q4)
tempfile dur 
save `dur'

********************************************************************************
** CREATE FIGURE
********************************************************************************
use `haverd', clear 
collapse (mean) mich cpiu reggas inflation_yearly inflation_yoy_forward inflation_yoy_forward2 reggas2 gas_infl, by(yearq)
sort yearq
tsset yearq
gen inflation_qoq=((cpiu-L4.cpiu)/L4.cpiu)*100
gen inflation_qoq_forward=((F4.cpiu-cpiu)/cpiu)*100
gen gas_infl_qoq=((reggas-L4.reggas)/L4.reggas)*100
drop if yearq<tq(2009q4)
drop if yearq>tq(2012q4)
tempfile haverd2
save `haverd2' 

use `dur', clear

merge 1:1 yearq using `haverd2', gen(havermatch)

sort yearq

** temp code to inspect data--forecast errors etc
*gen forecast_err=d_inflmedian_dur-inflation_yoy_forward2
*sum forecast_err, d
***

**similar edits here October 2019 MB
**changed output name Jan 2021 MB
** Graph: Median Inflation Expectations vs. Gas Price 
#delimit ; 
twoway 
(connected d_inflmedian_dur yearq, mcolor(black) msize(small) lcolor(black) yaxis(1))
(connected gas_infl_qoq yearq, mcolor(orange) msize(small) lcolor(orange) yaxis(2)),
ylabel(1.5(0.5)4, angle(horizontal) labsize(small) axis(1)) ytitle("Med. Infl. Expectation, Percent", axis(1) size(small))
ylabel(, angle(horizontal) labsize(small) axis(2)) ytitle("Four-Qtr. Gas Price Inflation (Percent)", axis(2) size(small)) 
tlabel(2009q4 "2009Q4" 2010q4 "2010Q4" 2011q4 "2011Q4" 2012q4 "2012Q4", angle(horizontal) labsize(small)) xtitle("") 
/*title("Figure 5. RAND{stMono:-}ALP Inflation Expectations vs. Gas Price Inflation", size(medsmall)) */
legend(region(lstyle(none)) rows(3) colfirst symx(*.4) size(small) 
order(1 "RAND{stMono:-}ALP Median Inflation Expectation (Durables Sample)" 2 "US Retail Gasoline Price, Four{stMono:-}Quarter Percent Change"))
/*note("Notes: Median inflation expectation refers to the median one{stMono:-}year{stMono:-}ahead inflation expectation for the given quarter." 
"The RAND{stMono:-}ALP median expectations are calculated using the baseline regression sample for durable goods spending." 
"Gas price inflation is the four{stMono:-}quarter percent change in the average US price of regular grade gasoline." 
"Source: Average US gasoline prices provided by US Energy Information Administration/Haver Analytics.", size(vsmall))*/
;
#delimit cr

graph export ../Figures/figureA6.png, as(png) replace

set scheme s1mono

#delimit ; 
twoway 
(connected d_inflmedian_dur yearq, mcolor(black) msize(small) lcolor(black) yaxis(1))
(connected gas_infl_qoq yearq, mcolor(gray) msize(small) lcolor(gray) yaxis(2)),
ylabel(1.5(0.5)4, angle(horizontal) labsize(small) axis(1)) ytitle("Med. Infl. Expectation, Percent", axis(1) size(small))
ylabel(, angle(horizontal) labsize(small) axis(2)) ytitle("Four-Qtr. Gas Price Inflation (Percent)", axis(2) size(small)) 
tlabel(2009q4 "2009Q4" 2010q4 "2010Q4" 2011q4 "2011Q4" 2012q4 "2012Q4", angle(horizontal) labsize(small)) xtitle("") 
/*title("Figure 5. RAND{stMono:-}ALP Inflation Expectations vs. Gas Price Inflation", size(medsmall)) */
legend(region(lstyle(none)) rows(3) colfirst symx(*.4) size(small) 
order(1 "RAND{stMono:-}ALP Median Inflation Expectation (Durables Sample)" 2 "US Retail Gasoline Price, Four{stMono:-}Quarter Percent Change"))
/*note("Notes: Median inflation expectation refers to the median one{stMono:-}year{stMono:-}ahead inflation expectation for the given quarter." 
"The RAND{stMono:-}ALP median expectations are calculated using the baseline regression sample for durable goods spending." 
"Gas price inflation is the four{stMono:-}quarter percent change in the average US price of regular grade gasoline." 
"Source: Average US gasoline prices provided by US Energy Information Administration/Haver Analytics.", size(vsmall))*/
;
#delimit cr

graph export ../Figures/figureA6_bw.png, as(png) replace
